Introduction to Python NumPy Array Slicing

Python NumPy Array Slicing

Array slicing refers to extracting a portion of an array by specifying a range of indices or using a specific pattern.

Slicing allows you to work with subsets of the original array without creating a copy, which can be useful for manipulating and analyzing data efficiently.

Here's how you can perform array slicing in NumPy:

One-Dimensional Arrays

For a one-dimensional array, slicing is similar to Python lists. You can use the colon : notation to specify the start, stop, and step size.

import numpy as np

# Create a 1-dimensional array
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# Slice a portion of the array
sliced_arr = arr[2:7]     # Elements from index 2 to 6 (7 is exclusive)
print(sliced_arr)         # Output: [3 4 5 6 7]

# Slice with a step size
step_arr = arr[1:9:2]     # Elements from index 1 to 8 with step size 2
print(step_arr)           # Output: [2 4 6 8]

# Slice from the beginning to a specific index
start_arr = arr[:5]       # Elements from the beginning to index 4 (5 is exclusive)
print(start_arr)          # Output: [1 2 3 4 5]

# Slice from a specific index to the end
end_arr = arr[6:]         # Elements from index 6 to the end
print(end_arr)            # Output: [7 8 9 10]

# Slice with negative indexing
neg_arr = arr[-5:-2]      # Elements from the fifth last element to the third last element
print(neg_arr)            # Output: [6 7 8]

Multi-Dimensional Arrays

For multi-dimensional arrays, you can slice along each dimension separately using the comma , notation.

import numpy as np

# Create a 2-dimensional array
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Slice rows and columns
sliced_rows = arr[1:3, :]      # Rows 1 and 2, all columns
print(sliced_rows)
# Output:
# [[4 5 6]
#  [7 8 9]]

sliced_cols = arr[:, 1:]      # All rows, columns 1 and onwards
print(sliced_cols)
# Output:
# [[2 3]
#  [5 6]
#  [8 9]]

# Combine row and column slicing
sliced_subset = arr[0:2, 1:]   # Rows 0 and 1, columns 1 and onwards
print(sliced_subset)
# Output:
# [[2 3]
#  [5 6]]

Negative Slicing

Negative slicing in NumPy allows you to extract elements from an array using negative indices.

It provides a convenient way to access elements from the end of the array while specifying a range or pattern.

Here's how you can perform negative slicing in NumPy:

One-Dimensional Arrays

For a one-dimensional array, negative slicing works similar to positive slicing using the colon : notation.

import numpy as np

# Create a 1-dimensional array
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

# Negative slicing
negative_slice = arr[-6:-2]  # Elements from the sixth last element to the third last element
print(negative_slice)       # Output: [5 6 7 8]

In the example above:

• The negative slice [-6:-2] corresponds to elements starting from the sixth last element (index -6) up to the third last element (index -2).

Multi-Dimensional Arrays

For multi-dimensional arrays, negative slicing can be applied along each dimension using negative indices.

import numpy as np

# Create a 2-dimensional array
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Negative slicing
negative_slice = arr[-2:, :-1]  # Last two rows, all columns except the last one
print(negative_slice)
# Output:
# [[4 5]
#  [7 8]]

In the example above:

• The negative slice [-2:, :-1] corresponds to the last two rows (index -2 onwards) and all columns except the last one (index :-1).